This study presents a hybrid evolution strategy–soft actor-critic (ES–SAC) controller for a reduced-degrees of freedom spatial (3D) humanoid gait model with predominantly sagittal-plane motion that focuses on arm-leg coordination. Policies are trained on flat terrain only and then evaluated on both flat and uneven ground under identical simulation settings. Arm-leg coordination is examined systematically in three modes (counter-phase with the legs [normal], in-phase [anti-normal], and fixed [passive]), and the results are compared with findings from human experiments. Whereas most prior studies evaluate policies primarily via reward curves, this work conducts a deep analysis using interpretable metrics aligned with human-like walking: speed-normalized power and torque, lateral and vertical deviations, and moment-balance terms. Simulation outcomes are reported quantitatively through these metrics rather than reward alone. Across five random seeds, a clear terrain-dependent trade-off appears between the swinging strategies: anti-normal attains higher forward speed and lower torque-per-speed, whereas normal provides better lateral tracking and lower power-per-speed on rough ground. Directional trends agree with human experiments (e.g., immobilized or in-phase arms raise metabolic cost), while numerical gaps reflect that the simulator measures mechanical power rather than metabolic energy. Within this framework, the impact of coordinated arm swing on balance and efficiency is quantified with a breadth and clarity uncommon in the literature.